Improvement of Georeferencing Accuracy of Lower Resolution Images Based on
High Resolution Feature Matching Technique
A.Z.MD. ZAHEDUL ISLAM
Bangladesh Space Research and Remote Sensing Organization (SPARRSO),
Agargaon, Sher-e-Bangla Nagar, Dhaka-1207, Bangladesh
E-mail: azmd_zahed@yahoo.com; Phone: 880-2-8127374; Fax: 880-2-8113080
Improvement of Georeferencing Accuracy of Lower Resolution Images Based on
High Resolution Feature Matching Technique
Abstract
A High Resolution Feature Matching (HRFM) technique is presented in this paper to
solve the problem of identifying well distributed Ground Control Points (GCP) on lower
resolution images. The technique is based on identification of GCPs on a higher
resolution image and then transferring the GCP cross-sections to the lower resolution
image based on nearest neighbor feature matching. This technique renders the
georeferencing accuracy to a sub-pixel level and is found very useful for georeferencing
lower resolution images including microwave images. The georeference accuracy based
on the HRFM technique is found to be less dependent on the resolution of the target
image which suggests a high degree of potential improvement of georeference accuracy
for low resolution images such as NOAA, MODIS etc.
Keywords: Georeferencing, Sub-pixel accuracy, Ground Control Points
1. Introduction
In general, positional accuracy of an image is in the order of its pixel dimension
provided that well distributed GCPs are used for georeferencing the image.
Identification of well distributed GCPs is a crucial aspect in the image georeferencing
process (O’Carroll 2010). For low resolution optical images and also for Synthetic
Aperture Radar (SAR) images this aspect becomes more crucial (Khlopenkov et al.
2010, Couloigner et al. 2002, Kumar et al. 2006). Researchers found out various ways
to deal with this crucial aspect. Rottensteiner et al. (2009) used a generic pushbroom
sensor model and strip adjustment approach to arrive at pixel-level accuracy; the
method reduces the number of GCPs but still depends on its distribution over specific
areas of the images. Khlopenkov et al. (2010) achieved better than 1/3 FOV geolocation
accuracy for AVHRR 1-km scenes based on an image matching technique using
MODIS images at 250-m resolution as reference; the efficiency of the method depends
on density and uniformity of GCP coverage. Couloigner et al. (2002) used an image and
topographic data matching technique to automatically identify GCPs in RADARSAT
images; the absolute geolocation accuracy from this method is questionable and largely
depends on the accuracy of the topographic database. Availability of topographic
database in usable form constrains the application of this technique in many cases.
Review of other techniques described by Willneff et al. (2011), Errico et al. (2011),
Weser et al. (2011), Fraser et al. (2011), Iisaka et al. (1996) and Eugenio et al. (2003)
along with those mentioned above suggests that even for achieving pixel level accuracy
based on operational techniques, identification of well distributed GCPs is still a basic
requirement.
Sub-pixel positional accuracy is one of the requirements of many applications. To
achieve sub-pixel positional accuracy, two requirements of GCP identification are to be
satisfied: GCP cross-section to a sub-pixel level and well distributed GCPs over the
image. Both these are long existing problems for moderate to low resolution images.
This paper describes a high resolution feature (feature derived from high resolution
images) matching technique to identify GCPs on lower resolution images to achieve
sub-pixel georeferencing accuracy.
2. The High Resolution Feature Matching (HRFM) Technique
A sharp GCP cross-section visible on high resolution image may not be visible on
lower resolution image, but other bigger features near the GCP may be visible on both
the images. Therefore, digitizing the nearest neighbor features of the GCP along with
the GCP cross-section visible on high resolution image and then matching these
digitized features with those on the lower resolution image will help to locate the GCP
cross-section on the later image. The steps of application of this technique are given
below.
i.
Identification of the GCP location in the high resolution image for GCPs that
have geographic features around it (nearest neighbor features) that are visible on
both the high and the lower resolution images. The geographic features may be
roads, canals, rivers etc. Figure 1 shows an example of identification of GCP
locations.
ii.
Digitization of the nearest neighbor geographic features along with the GCP
cross-section on high resolution image.
iii.
Matching of the vector layer of the features obtained in step (ii) with the
geographic features on the lower resolution image. This matching can be carried
out in different ways. The procedure followed in this paper is given below.
a. Primary georeferencing of lower resolution image referring to high
resolution image based on conventional procedure (visual interpretation
for identification of the approximate locations of the GCPs). This allows
the approximate estimation of the scale and rotation of the lower
resolution image to that of the high resolution image and helps to match
the features digitized from the high resolution image.
b. Matching of the vector layer through manual shifting.
iv.
Figure 2 illustrates execution of steps (ii) and (iii). However, in some other cases
simple manual shifting may not be satisfactory and the vector layer may need to
be rotated and/or scaled.
v.
Picking up the reference and input coordinate values from the GCP cross-section
on high resolution and lower resolution images respectively and georeferencing
the lower resolution image based on these values. Second order polynomial
transformation and nearest neighbor resampling technique were used for
georeferencing the images.
3. Application and Results
The applicability of the HRFM technique was tested using two high resolution
images having similar resolution. Aerial photos captured at a scale of 1: 25000 and
scanned at 1200 Dot Per Inch (DPI) to render 0.5 m pixels were used as high resolution
reference image. The aerial photographs were orthorectified using ERDAS Imagine
software. The Digital Elevation Model (DEM) used for orthorectification was generated
during the orthorectification process. The GCPs used for orthorectification were
collected using DGPS. Quick Bird image of 0.6 m resolution was used as input data.
Since both the reference and input images were of similar high spatial resolution,
locating of GCP using the HRFM technique should hypothetically be errorless provided
that there is no error introduced by topographic variation since the input image is not
underwent orthorectification processes using the HRFM technique. Three flat areas
were selected as test area to avoid the error in georeferencing due topographic variation.
Well-distributed GCPs were located on the Quick Bird image based on the reference
image (aerial photo mosaic). Some of the GCPs were used as the check points to
quantify the positional error of the Quick Bird images after georeferencing. Table 1
presents the positional error of the Quick Bird images. It is seem from table 1 that after
georeferencing highest positional error of 0.17 m Circular Error at 90% (CE90)
confidence level was observed based on the check points. This error margin included
the human error involved for application (digitization, feature matching, etc.) of the
HRFM technique and cannot be avoided. The quantitative values of the error indicate
that the HRFM technique can be applied to georeference images with sub-pixel
accuracy.
The HRFM technique was applied to Landsat TM image of two different frames
(path/row 137/43 and 138/44) and two RADARSAT ScanSAR WideA (SCWA)
images covering approximately the whole of Bangladesh. The aerial photos mentioned
above were used as high resolution reference image for georeferencing the Landsat TM
and RADARSAT SCWA images. To verify the positional accuracy of the
georeferenced images, the method devised by Cuartero et al. (2010) was used. The
positions of center lines of roads and small-width rivers digitized from the aerial photos
were used as independent check lines and were compared with the same digitized from
the georeferenced Landsat TM and RADARSAT images. Table 2 presents the
positional accuracy of the Landsat TM and RADARSAT images. Shifts of 4.1 m CE90
and 7.2 m CE90 were seen for Landsat TM and RADARSAT images respectively.
Neglecting the digitization error, these values can be treated as the positional accuracy
of the georeferenced Landsat TM and RADARSAT SCWA images. The errors of
georeferencing the two types of images based on the HRFM technique is seen to be less
dependent of the image resolutions (30 m and 100 m respectively for Landsat TM and
RADARSAT SCWA images).
4. Conclusion
The HRFM technique solves the problem of well distributed GCP identification on
lower resolution images as well as it improves the georeference accuracy of lower
resolution images in sub-pixel level. This technique is very useful for georeferencing
microwave images where identification of GCP is very difficult. Application of the
technique is more time consuming than the conventional approach as it involves
additional work for generation of nearest neighbor feature layer from high resolution
images and then matching it with the features of the lower resolution images.
Availability of a high resolution image may be a problem for application of the
technique. The absolute geographic accuracy of the high resolution image may be
another problem. However, identification of GCP cross-sections using high resolution
image having a certain level of geographic accuracy and then collection of coordinate
values of the GCP cross-sections using DGPS can solve the accuracy problem of the
high resolution image.
Less dependency of georeferencing error on image resolution hypothetically infer that
the HRFM technique will likely improve the georeferencing accuracy of low resolution
images (NOAA, MODIS etc.). The hypothesis is being tested along with some other
aspects of operational application of the HRFM technique.
Acknowledgements
This work was carried out using the facilities (hardware, software, data etc.) of
Bangladesh Space Research and Remote Sensing Organization (SPARRSO). The author
is grateful to Dr. Timothy Warner, West Virginia University, USA for his valuable
suggestions to improve the description of this paper.
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Title of tables
Table 1 Positional error of the Quick Bird images.
Table 2 Positional error of the Landsat TM and RADARSAT image.
Test Area
Area,
Sq. km
No. of
GCPs
No. of Check
points
Positional Error
(CE90), m
Area 1
302
24
10
0.16
Area 2
283
21
10
0.17
Area 3
332
29
13
0.17
Image
Area/Frame
No. of
GCPs
No. of Check
lines
Positional
Shift (CE90),
m
Landsat TM
Path/Row: 137/43 and
138/44
32
17
4.1
RADARSAT
Whole Bangladesh
(approximately)
69
22
7.2
Figure captions:
Figure 1. (a) Identification of GCP locations, green circle, in high resolution image
(aerial photo); (b) The GCPs are not visible in lower resolution image (Landsat TM).
The GCP locations have geographic features around it that are visible on both the (c)
high resolution image and the (d) lower resolution image.
Figure 2. Matching of the vector layer (seville orange) of geographic features generated
through digitization on (a) high resolution image with the geographic features on (b)
lower resolution image. Green circle shows the GCP cross-sections.
1(a)
Canal
Road
1(c)
1(b)
Canal
Road
1(d)
2(a)
2(b)